docs: def voronoi(df, projection=None, edgecolor='black',
              clip=None,
              hue=None, scheme=None, k=5, cmap='viridis', categorical=False, vmin=None, vmax=None,
              legend=False, legend_kwargs=None, legend_labels=None,
              extent=None, figsize=(8, 6), ax=None,
              **kwargs):
      """
      Geospatial Voronoi diagram.
  
      Parameters
      ----------
      df : GeoDataFrame
          The data being plotted.
      projection : geoplot.crs object instance, optional
          A geographic projection. For more information refer to `the tutorial page on projections
          <https://nbviewer.jupyter.org/github/ResidentMario/geoplot/blob/master/notebooks/tutorials/Projections.ipynb>`_.
      hue : None, Series, GeoSeries, iterable, or str, optional
          Applies a colormap to the output points.
      categorical : boolean, optional
          Set to ``True`` if ``hue`` references a categorical variable, and ``False`` (the default) otherwise. Ignored
          if ``hue`` is left unspecified.
      scheme : None or {"quantiles"|"equal_interval"|"fisher_jenks"}, optional
          Controls how the colormap bin edges are determined. Ignored if ``hue`` is left unspecified.
      k : int or None, optional
          Ignored if ``hue`` is left unspecified. Otherwise, if ``categorical`` is False, controls how many colors to
          use (5 is the default). If set to ``None``, a continuous colormap will be used.
      cmap : matplotlib color, optional
          The `matplotlib colormap <http://matplotlib.org/examples/color/colormaps_reference.html>`_ to be used.
          Ignored if ``hue`` is left unspecified.
      vmin : float, optional
          Values below this level will be colored the same threshold value. Defaults to the dataset minimum. Ignored
          if ``hue`` is left unspecified.
      vmax : float, optional
          Values above this level will be colored the same threshold value. Defaults to the dataset maximum. Ignored
          if ``hue`` is left unspecified.
      legend : boolean, optional
          Whether or not to include a legend. Ignored if neither a ``hue`` nor a ``scale`` is specified.
      legend_values : list, optional
          The values to use in the legend. Defaults to equal intervals. For more information see `the Gallery demo
          <https://residentmario.github.io/geoplot/examples/largest-cities-usa.html>`_.
      legend_labels : list, optional
          The names to use in the legend. Defaults to the variable values. For more information see `the Gallery demo
          <https://residentmario.github.io/geoplot/examples/largest-cities-usa.html>`_.
      legend_kwargs : dict, optional
          Keyword arguments to be passed to `the underlying legend <http://matplotlib.org/users/legend_guide.html>`_.
      extent : None or (minx, maxx, miny, maxy), optional
          Used to control plot x-axis and y-axis limits manually.
      figsize : tuple, optional
          An (x, y) tuple passed to ``matplotlib.figure`` which sets the size, in inches, of the resultant plot.
      ax : AxesSubplot or GeoAxesSubplot instance, optional
          A ``matplotlib.axes.AxesSubplot`` or ``cartopy.mpl.geoaxes.GeoAxesSubplot`` instance. Defaults to a new axis.
      kwargs: dict, optional
          Keyword arguments to be passed to the underlying ``matplotlib`` `Line2D objects
          <http://matplotlib.org/api/lines_api.html#matplotlib.lines.Line2D>`_.
  
      Returns
      -------
      AxesSubplot or GeoAxesSubplot instance
          The axis object with the plot on it.
  
      Examples
      --------
  
      The neighborhood closest to a point in space is known as its `Voronoi region
      <https://en.wikipedia.org/wiki/Voronoi_diagram>`_. Every point in a dataset has a Voronoi region, which may be
      either a closed polygon (for inliers) or open infinite region (for points on the edge of the distribution). A
      Voronoi diagram works by dividing a space filled with points into such regions and plotting the result. Voronoi
      plots allow efficient assessmelt of the *density* of points in different spaces, and when combined with a
      colormap can be quite informative of overall trends in the dataset.
  
      The ``geoplot`` ``voronoi`` is a spatially aware application of this technique. It compares well with the more
      well-known ``choropleth``, which has the advantage of using meaningful regions, but the disadvantage of having
      defined those regions beforehand. ``voronoi`` has fewer requirements and may perform better when the number of
      observations is small. Compare also with the quadtree technique available in ``aggplot``.
  
      A basic ``voronoi`` specified data and, optionally, a projection. We overlay geometry to aid interpretability.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000))
          gplt.polyplot(boroughs, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-simple.png
  
      ``hue`` parameterizes the color, and ``cmap`` controls the colormap.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds')
          gplt.polyplot(boroughs, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-cmap.png
  
      Add a ``clip`` of iterable geometries to trim the ``voronoi`` against local geography.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds',
                            clip=boroughs.geometry)
          gplt.polyplot(boroughs, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-clip.png
  
      ``legend`` adds a a ``matplotlib`` `Legend
      <http://matplotlib.org/api/legend_api.html#matplotlib.legend.Legend>`_. This can be tuned even further using the
      ``legend_kwargs`` argument. Other keyword parameters are passed to the underlying ``matplotlib`` `Polygon patches
      <http://matplotlib.org/api/patches_api.html#matplotlib.patches.Polygon>`_.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds',
                            clip=boroughs.geometry,
                            legend=True, legend_kwargs={'loc': 'upper left'},
                            linewidth=0.5, edgecolor='white',
                           )
          gplt.polyplot(boroughs, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-kwargs.png
  
      Change the number of bins by specifying an alternative ``k`` value. To use a continuous colormap, explicitly
      specify ``k=None``.  You can change the binning sceme with ``scheme``. The default is ``quantile``, which bins
      observations into classes of different sizes but the same numbers of observations. ``equal_interval`` will
      creates bins that are the same size, but potentially containing different numbers of observations. The more
      complicated ``fisher_jenks`` scheme is an intermediate between the two.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000),
                            hue='NUMBER OF PERSONS INJURED', cmap='Reds', k=5, scheme='fisher_jenks',
                            clip=boroughs.geometry,
                            legend=True, legend_kwargs={'loc': 'upper left'},
                            linewidth=0.5, edgecolor='white',
                           )
          gplt.polyplot(boroughs, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-scheme.png
  
      If your variable of interest is already `categorical
      <http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_, specify ``categorical=True`` to
      use the labels in your dataset directly.
  
      .. code-block:: python
  
          ax = gplt.voronoi(injurious_collisions.head(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds',
               edgecolor='white', clip=boroughs.geometry,
               linewidth=0.5, categorical=True
               )
          gplt.polyplot(boroughs, linewidth=1, ax=ax)
  
      .. image:: ../figures/voronoi/voronoi-multiparty.png
      """
  
      # Initialize the figure.
      fig = _init_figure(ax, figsize)
  
      if projection:
          # Properly set up the projection.
          projection = projection.load(df, {
              'central_longitude': lambda df: np.mean(np.array([p.x for p in df.geometry.centroid])),
              'central_latitude': lambda df: np.mean(np.array([p.y for p in df.geometry.centroid]))
          })
  
          # Set up the axis.
          if not ax:
              ax = plt.subplot(111, projection=projection)
  
      else:
          if not ax:
              ax = plt.gca()
  
      # Clean up patches.
      _lay_out_axes(ax, projection)
  
      # Immediately return if input geometry is empty.
      if len(df.geometry) == 0:
          return ax
  
      # Set extent.
      xs, ys = [p.x for p in df.geometry.centroid], [p.y for p in df.geometry.centroid]
      extrema = np.min(xs), np.max(xs), np.min(ys), np.max(ys)
      _set_extent(ax, projection, extent, extrema)
  
      # Validate hue input.
      hue = _validate_hue(df, hue)
  
      # Generate the coloring information, if needed. Follows one of two schemes, categorical or continuous,
      # based on whether or not ``k`` is specified (``hue`` must be specified for either to work).
      if k is not None:
          # Categorical colormap code path.
          categorical, k, scheme = _validate_buckets(categorical, k, scheme)
  
          if hue is not None:
              cmap, categories, hue_values = _discrete_colorize(categorical, hue, scheme, k, cmap, vmin, vmax)
              colors = [cmap.to_rgba(v) for v in hue_values]
  
          else:
              colors = ['None']*len(df)
  
      elif k is None and hue is not None:
          # Continuous colormap code path.
          hue_values = hue
          cmap = _continuous_colormap(hue_values, cmap, vmin, vmax)
          colors = [cmap.to_rgba(v) for v in hue_values]
  
      elif 'facecolor' in kwargs:
          colors = [kwargs.pop('facecolor')]*len(df)
      else:
          colors = ['None']*len(df)
  
      # Finally we draw the features.
      geoms = _build_voronoi_polygons(df)
      if projection:
          for color, geom in zip(colors, geoms):
              features = ShapelyFeature([geom], ccrs.PlateCarree())
              ax.add_feature(features, facecolor=color, edgecolor=edgecolor, **kwargs)
  
          if clip is not None:
              clip_geom = _get_clip(ax.get_extent(crs=ccrs.PlateCarree()), clip)
              feature = ShapelyFeature([clip_geom], ccrs.PlateCarree())
              ax.add_feature(feature, facecolor=(1,1,1), linewidth=0, zorder=100)
  
      else:
          for color, geom in zip(colors, geoms):
              feature = descartes.PolygonPatch(geom, facecolor=color, edgecolor=edgecolor, **kwargs)
              ax.add_patch(feature)
  
          if clip is not None:
              clip_geom = _get_clip(ax.get_xlim() + ax.get_ylim(), clip)
              ax = polyplot(gpd.GeoSeries(clip_geom), facecolor='white', linewidth=0, zorder=100,
                            extent=ax.get_xlim() + ax.get_ylim(), ax=ax)
  
      # Add a legend, if appropriate.
      if legend and k is not None:
          _paint_hue_legend(ax, categories, cmap, legend_labels, legend_kwargs, figure=True)
      elif legend and k is None and hue is not None:
          _paint_colorbar_legend(ax, hue_values, cmap, legend_kwargs)
  
      return ax